Electron microscopic imaging measurement method, device, equipment, medium and program product

Abstract

The invention relates to the technical field of semiconductor measurement, in particular to an electron microscopic imaging measurement method, device and equipment, a medium and a program product. The method comprises the following steps: acquiring a detection image of a to-be-processed target object; processing the detection image based on a pre-constructed image conversion model to obtain a template image matched with the detection image; determining target information of a target area in the template image based on a preset image analysis algorithm, wherein the target area comprises a measurement target and a measurement target edge; generating a measurement target frame corresponding to the target area according to the target information, applying the measurement target frame to the detection image so as to determine a local area including the target object in the detection image, and imaging the local area so as to measure the target object. By adopting the method, the overall efficiency and accuracy of the measurement process can be improved.

Description

Technical Field

[0001] The present application relates to the field of semiconductor measurement technology, and in particular to an electron microscopic imaging measurement method, device, equipment, medium and program product. Background Art

[0002] The electron microscope is an important instrument for measuring critical dimensions in the semiconductor manufacturing process. It can measure the size of the pattern formed during the photolithography process with high precision, which is crucial to ensuring the performance and stability of semiconductor chips. The main functions and applications of the electron microscope include: high-resolution imaging, critical dimension measurement, defect detection, process control, and three-dimensional measurement. Electron microscopes are used as front-end inspection equipment in the field of semiconductor integrated circuits. They can realize the fast and accurate automatic focusing function of the equipment, and provide image quality assurance for subsequent visual positioning, critical dimension measurement, defect detection and other functions.

[0003] In the related technologies, the measurement algorithms in electron microscopy imaging detection are a series of algorithms used to measure and analyze various features and parameters on semiconductor wafers. These algorithms are crucial in the semiconductor manufacturing process, especially in wafer quality control, defect detection and process optimization. Among them, common algorithms and technologies mainly include image processing algorithms, geometric measurements, statistical analysis, model-based algorithms, and measurement algorithms for optical and other types of sensing technologies. At present, most measurement algorithms are based on traditional algorithms, and technicians need to manually set recipes to locate the measurement position and measurement parameters according to the set imaging conditions.

[0004] However, the current measurement methods have the following technical problems: the existing measurement methods are low in efficiency and accuracy and need to be optimized. Summary of the invention

[0005] Based on this, it is necessary to provide an electron microscopy imaging measurement method, device, equipment, medium and program product that can improve the efficiency and accuracy of electron microscopy image measurement in response to the above technical problems.

[0006] In a first aspect, the present application provides an electron microscopic imaging measurement method. The method comprises:

[0007] Acquire a detection image of a target object to be processed;

[0008] Processing the detection image based on a pre-built image conversion model to obtain a template image matching the detection image;

[0009] Determine target information of a target area in the template image based on a preset image analysis algorithm, wherein the target area includes a measurement target and a measurement target edge;

[0010] A measurement target frame corresponding to the target area is generated according to the target information, and the measurement target frame is applied to the detection image to determine a local area containing the target object in the detection image, and the local area is imaged to achieve measurement of the target object.

[0011] In one embodiment, the determining of target information of a target area in the template image based on a preset image analysis algorithm, wherein the target area includes a measurement target and a measurement target edge, includes:

[0012] Traversing the template image, and determining a plurality of connected domains in the template image based on pixel value differences in the template image;

[0013] A region identifier is assigned to the connected domain, where the region identifier is a unique identifier.

[0014] In one embodiment, the determining of target information of a target area in the template image based on a preset image analysis algorithm, wherein the target area includes a measurement target and a measurement target edge, includes:

[0015] Identify the boundary of the connected domain based on a preset contour extraction algorithm to obtain initial boundary information;

[0016] A geometric approximation process is performed on the boundary of the connected domain according to the initial boundary information to obtain boundary information of the connected domain that meets preset geometric constraints.

[0017] In one embodiment, the determining of target information of a target area in the template image based on a preset image analysis algorithm, wherein the target area includes a measurement target and a measurement target edge, includes:

[0018] Acquire the measurement target edge information of the target area, and acquire the pixel change trend curve of the measurement target edge;

[0019] The edge type of the edge of the measurement target is determined according to the pixel change trend curve.

[0020] In one embodiment, before processing the detection image based on the pre-built image conversion model to obtain a template image matching the detection image, the method further includes:

[0021] Acquire a sample data set, wherein the sample data set includes historical detection images and corresponding graphic data system templates;

[0022] Performing pattern matching between the historical detection image and the graphic data system template, and generating several types of sample template images based on the graphic data system template;

[0023] A label data set is constructed based on the historical detection images and the sample template images in a preset ratio, and a pre-constructed loop generation discriminant network model is trained by using the label data set to obtain the image conversion model.

[0024] In one embodiment, the label data set is constructed based on the historical detection images and the sample template images of a preset ratio, and a pre-constructed cycle generation discriminant network model is trained by the label data set to obtain the image conversion model, including:

[0025] The cyclic generation discriminant network model includes two generation networks and a corresponding discriminant network, wherein the generation network is used to realize the conversion generation between the detection image and the template image, and the discriminant network is used to realize the authenticity discrimination of the generated image;

[0026] An optimization objective function is constructed, and the training process of the cyclic generation and discrimination network model is constrained based on the optimization objective function. The optimization objective function includes the corresponding optimization objective function between the generation network and the discrimination network, and also includes a cyclic loss function between the generation networks.

[0027] In a second aspect, the present application also provides an electron microscopic imaging measurement device. The device comprises:

[0028] A detection image module, used to obtain a detection image of a target object to be processed;

[0029] A template image module, used for processing the detection image based on a pre-built image conversion model to obtain a template image matching the detection image;

[0030] A target detection module, used to determine target information of a target area in the template image based on a preset image analysis algorithm, wherein the target area includes a measurement target and a measurement target edge;

[0031] A measurement parameter module is used to generate a measurement target frame corresponding to the target area according to the target information, apply the measurement target frame to the detection image to determine a local area containing the target object in the detection image, and image the local area to achieve measurement of the target object.

[0032] In one embodiment, the target detection module includes:

[0033] A connected domain module, used for traversing the template image and determining a plurality of connected domains in the template image based on pixel value differences in the template image;

[0034] The area identification module is used to assign an area identification to the connected domain, where the area identification is a unique identification.

[0035] In one embodiment, the target detection module includes:

[0036] An initial boundary module, used to identify the boundary of the connected domain based on a preset contour extraction algorithm and obtain initial boundary information;

[0037] The geometric approximation module is used to perform geometric approximation processing on the boundary of the connected domain according to the initial boundary information, and obtain the boundary information of the connected domain that meets the preset geometric constraint conditions.

[0038] In one embodiment, the target detection module includes:

[0039] A pixel trend module, used to obtain the measurement target edge information of the target area and obtain a pixel change trend curve of the measurement target edge;

[0040] The edge type module is used to determine the edge type of the edge of the measurement target according to the pixel change trend curve.

[0041] In one embodiment, before the template image module, it also includes:

[0042] A sample data module, used to obtain a sample data set, wherein the sample data set includes a historical detection image and a corresponding graphic data system template;

[0043] A pattern matching module, used for performing pattern matching between the historical detection image and the graphic data system template, and generating several types of sample template images based on the graphic data system template;

[0044] The conversion model module is used to construct a label data set based on the historical detection images and the sample template images of a preset ratio, and train the pre-constructed loop generation discriminant network model through the label data set to obtain the image conversion model.

[0045] In one embodiment, the conversion model module includes:

[0046] The cyclic generation discriminant network model includes two generation networks and a corresponding discriminant network, wherein the generation network is used to realize the conversion generation between the detection image and the template image, and the discriminant network is used to realize the authenticity discrimination of the generated image;

[0047] The optimization function module is used to construct an optimization objective function, and constrain the training process of the cyclic generation and discrimination network model based on the optimization objective function. The optimization objective function includes the corresponding optimization objective function between the generation network and the discrimination network, and also includes the cyclic loss function between the generation networks.

[0048] In a third aspect, the present application further provides a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps in the electron microscopy imaging measurement method described in any one of the embodiments in the first aspect are implemented.

[0049] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps in the electron microscopy imaging measurement method as described in any one of the embodiments in the first aspect are implemented.

[0050] In a fifth aspect, the present application further provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps in the electron microscopy imaging measurement method described in any one of the embodiments in the first aspect are implemented.

[0051] The above-mentioned electron microscopic imaging measurement method, device, equipment, medium and computer program product can achieve the beneficial effects of the technical problems in the corresponding background technology by deducing the technical features in the exclusive rights:

[0052] The present application provides an electron microscopic imaging measurement method, including obtaining a detection image of a target object to be processed; processing the detection image based on a pre-constructed image conversion model to obtain a template image matching the detection image; determining target information of a target area in the template image based on a preset image analysis algorithm, wherein the target area includes a measurement target and a measurement target edge; generating a measurement target frame corresponding to the target area according to the target information, and applying the measurement target frame to the detection image to achieve measurement of the target object. In implementation, after obtaining the detection image of the target object to be processed, the detection image is processed by applying a pre-constructed image conversion model, so that the detection image is converted into a template image. At this time, the obtained template image has stronger standardization and consistency, so that the imaging measurement method can adapt to different imaging conditions, improve the stability of target recognition and measurement, and on the other hand, the template image can weaken the influence of noise information in the detection image on the measurement, improve the efficiency and accuracy of the measurement of the target object, enhance the identification of the target area and edge, and help the subsequent step of extracting the target information, further improving the speed and accuracy of the measurement. Finally, a measurement target frame can be generated based on the obtained target information and applied to the detection image to achieve measurement processing of the target object, thereby improving the overall efficiency and accuracy of the measurement process. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0054] Figure 1 FIG. 1 is a schematic diagram of a first process of an electron microscopic imaging measurement method in an embodiment.

[0055] Figure 2 Schematic diagram of the image acquisition process in the measurement process.

[0056] Figure 3 FIG. 4 is a second flow chart of an electron microscopy imaging measurement method in another embodiment.

[0057] Figure 4 FIG. 4 is a third flow chart of an electron microscopy imaging measurement method in another embodiment.

[0058] Figure 5 FIG. 4 is a schematic diagram of a fourth flow chart of an electron microscopic imaging measurement method in another embodiment.

[0059] Figure 6 A schematic diagram of edge type classification in one embodiment.

[0060] Figure 7 Schematic diagram of the correspondence between pixel change trend curve and edge type in one embodiment.

[0061] Figure 8 FIG. 5 is a fifth flow chart of an electron microscopy imaging measurement method in another embodiment.

[0062] Fig. 9 FIG. 6 is a sixth flow chart of an electron microscopy imaging measurement method in another embodiment.

[0063] Fig.10 A schematic diagram of a flow chart for cyclically generating a discriminant network in one embodiment.

[0064] Fig.11 Schematic diagram of the architecture of the network generated for deep learning.

[0065] Fig.12 Schematic diagram of the architecture of the deep learning discriminant network.

[0066] Fig.13 FIG. 4 is a structural block diagram of an electron microscopic imaging measurement device in an embodiment.

[0067] Fig.14FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0068] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0069] In the related technologies, the measurement algorithms in electron microscopy imaging detection are a series of algorithms used to measure and analyze various features and parameters on semiconductor wafers. These algorithms are crucial in the semiconductor manufacturing process, especially in wafer quality control, defect detection and process optimization. Among them, common algorithms and technologies mainly include image processing algorithms, geometric measurements, statistical analysis, model-based algorithms, and measurement algorithms for optical and other types of sensing technologies. At present, most measurement algorithms are based on traditional algorithms, and technicians need to manually set recipes to locate the measurement position and measurement parameters according to the set imaging conditions.

[0070] However, the current measurement methods have the following technical problems: the existing measurement methods are low in efficiency and accuracy and need to be optimized.

[0071] Based on this, the embodiments of the present application provide an electron microscopy imaging measurement method, device, equipment, medium and program product.

[0072] In one embodiment, Figure 1 and Figure 2 As shown, an electron microscopic imaging measurement method is provided. This embodiment uses the method applied to a terminal as an example for illustration. It can be understood that the method can also be applied to a server, and can also be applied to a system including a terminal and a server, and is implemented through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0073] Step 102: Acquire a detection image of the target object to be processed.

[0074] Step 104: Process the detection image based on a pre-built image conversion model to obtain a template image that matches the detection image.

[0075] Step 106: Determine target information of a target area in the template image based on a preset image analysis algorithm, where the target area includes a measurement target and a measurement target edge.

[0076] Step 108: Generate a measurement target frame corresponding to the target area according to the target information, apply the measurement target frame to the detection image to determine a local area containing the target object in the detection image, and image the local area to achieve measurement of the target object.

[0077] The measurement target frame can refer to a bounding box used to identify and locate the area to be measured in electron microscopy imaging. The measurement target frame is usually a regular geometric shape frame, such as a rectangular frame, which is used to accurately select the measurement target and its edges in the image. The main functions of the measurement target frame are: to clarify the position of the measurement target in the image; to define the boundaries of the measurement target and determine the edges of the measurement target to facilitate subsequent geometric measurement and analysis; to assist algorithms in achieving measurement, reduce manual intervention, and improve measurement efficiency and accuracy.

[0078] Exemplarily, the terminal can extract key information of the target area from the template image through an image analysis algorithm, such as the target position, size, shape and edge features. Subsequently. The terminal can generate a measurement target frame, such as a rectangular frame, based on the target information, so that the measurement target and its edges can be framed by the measurement target frame. At this time, the size and position of the measurement target are determined by the geometric features of the target. In this way, the terminal can map the measurement target frame to the original detection image based on the mapping relationship between the algorithm space coordinate system and the real image coordinate system to ensure that the position and size of the measurement target frame are consistent with the target in the detection image. Subsequently, in the processing of the detection image, the terminal performs more accurate imaging and measurement of the target in the local area framed by the measurement target frame, obtains the measurement result of the target and outputs it, so as to realize subsequent quality control, defect detection or process optimization and other steps.

[0079] As a specific example, the application of this solution to microscopic measurement of the circuit structure of a semiconductor device is used as an example to illustrate: first, the image analysis algorithm is used to determine the area where the circuit structure to be inspected is located and the image edge of the circuit structure; then a measurement target frame is generated according to the size and shape of the circuit structure; the measurement target frame is mapped to the inspection image of the circuit, and the selected area of ​​the measurement target frame is locally focused and imaged to obtain information such as the size and connection of the local circuit structure; finally, the measurement result of the circuit structure is output.

[0080] In the above-mentioned electron microscopic imaging measurement method, reasonable deduction is performed in combination with the technical features in the embodiment to achieve the following beneficial effects that can solve the technical problems raised in the background technology:

[0081] The present application provides an electron microscopic imaging measurement method, including obtaining a detection image of a target object to be processed; processing the detection image based on a pre-constructed image conversion model to obtain a template image matching the detection image; determining target information of a target area in the template image based on a preset image analysis algorithm, wherein the target area includes a measurement target and a measurement target edge; generating a measurement target frame corresponding to the target area according to the target information, applying the measurement target frame to the detection image to determine a local area containing the target object in the detection image, and imaging the local area to achieve measurement of the target object. In implementation, after obtaining the detection image of the target object to be processed, the detection image is processed by applying a pre-constructed image conversion model, so that the detection image is converted into a template image. At this time, the obtained template image has stronger standardization and consistency, so that the imaging measurement method can adapt to different imaging conditions, improve the stability of target recognition and measurement, and on the other hand, the template image can weaken the influence of noise information in the detection image on the measurement, improve the efficiency and accuracy of the measurement of the target object, enhance the identification of the target area and edge, and help the subsequent step of extracting the target information, further improving the speed and accuracy of the measurement. Finally, a measurement target frame can be generated based on the obtained target information and applied to the detection image to achieve measurement processing of the target object, thereby improving the overall efficiency and accuracy of the measurement process.

[0082] In one embodiment, if Figure 3 As shown, step 106 includes:

[0083] Step 302: traverse the template image, and determine a number of connected domains in the template image based on pixel value differences in the template image.

[0084] Step 304: assigning a region identifier to the connected domain, where the region identifier is a unique identifier.

[0085] In this embodiment, in the process of detecting and identifying the target in the template image, a region identifier is assigned to each detected connected domain, and the unique identifier helps to improve the efficiency and accuracy of target recognition in complex images.

[0086] In one embodiment, if Figure 4 As shown, step 106 includes:

[0087] Step 402: Identify the boundary of the connected domain based on a preset contour extraction algorithm to obtain initial boundary information.

[0088] Step 404: performing geometric approximation processing on the boundary of the connected domain according to the initial boundary information, and obtaining boundary information of the connected domain that satisfies preset geometric constraints.

[0089] In this embodiment, in the process of performing boundary recognition on the target, the boundary contour is processed by using geometric approximation processing, which helps to improve the detection efficiency of boundary information.

[0090] In one embodiment, if Figure 5 As shown, step 106 includes:

[0091] Step 502: Obtain the measurement target edge information of the target area, and obtain the pixel change trend curve of the measurement target edge.

[0092] Step 504: Determine the edge type of the edge of the measurement target according to the pixel change trend curve.

[0093] Specifically, for example, Figure 2 , Figure 6 and Figure 7 As shown, the terminal can traverse the image and mark all measurement targets, measurement target edges and background areas according to different pixel values. The three different areas are assigned unique labels. Subsequently, the contour extraction algorithm can be used to identify the boundaries of each connected domain, and the extracted contours can be approximated by polygons to simplify the contour shape. In this way, the terminal can check whether the approximated polygon meets the conditions of a rectangle, that is, the four corners are right angles, and the opposite sides are parallel and equal in length. For rectangles that meet the conditions, the center and x-direction width are calculated.

[0094] For example, Figure 6 As shown in , the template image can be divided into dark image with bright edge, dark image, bright image and bright image with bright edge. The terminal can obtain the pixel change information of the measurement target in the x direction according to the measurement target position, such as Figure 7 As shown in the figure, the upper part of the figure shows four edge types, and the lower part shows the change trend curve of the corresponding edge x-direction pixels. In the two-color bright pattern, the grayscale is 128 and 255, of which 128 is the background grayscale and 255 is the pattern grayscale; in the two-color dark pattern, the grayscale is 0 and 128, of which 128 is the background grayscale and 0 is the pattern grayscale; in the three-color bright-edge dark pattern, the grayscale is 0, 128 and 255, of which 128 is the background grayscale, 0 is the pattern grayscale, and 255 is the bright edge; in the three-color bright-edge bright pattern, the grayscale is 128, 200, 255, of which 128 is the background grayscale, 200 is the pattern grayscale, and 255 is the bright edge. The edge type can be quickly determined based on the pixel change trend curve.

[0095] In this embodiment, in the process of identifying the edge type of the target, the edge type can be determined by the pixel change trend curve, which helps to improve the efficiency of edge type detection.

[0096] In one embodiment, if Figure 8As shown, before step 104, the following steps are also included:

[0097] Step 802: Acquire a sample data set, wherein the sample data set includes historical detection images and corresponding graphic data system templates.

[0098] Step 804: Pattern matching is performed between the historical detection image and the graphic data system template, and several types of sample template images are generated based on the graphic data system template.

[0099] Step 806: construct a label data set based on the historical detection images and the sample template images of a preset ratio, and train the pre-constructed loop generation discriminant network model through the label data set to obtain the image conversion model.

[0100] In this embodiment, in the training of the image conversion model, the label data set is constructed by matching historical detection images and graphic data system templates, which helps to improve the conversion accuracy of the image conversion model finally trained, so that the obtained template image meets the processing requirements of the graphic data system.

[0101] In one embodiment, if Fig. 9 As shown, step 806 includes:

[0102] The cyclic generation discriminant network model includes two generation networks and a corresponding discriminant network, wherein the generation network is used to realize the conversion generation between the detection image and the template image, and the discriminant network is used to realize the authenticity discrimination of the generated image;

[0103] Step 902: construct an optimization objective function, and constrain the training process of the cyclic generation discriminant network model based on the optimization objective function, wherein the optimization objective function includes the corresponding optimization objective function between the generation network and the discriminant network, and also includes a cyclic loss function between the generation networks.

[0104] Specifically, the terminal can perform pattern matching between the SEM image and the GDS template, and generate template images corresponding to four types of edge SEM images according to the information in the GDS template, such as Figure 6 As shown in the figure, there are dark image bright edge, dark image, bright image and bright image bright edge, with 500 edge images of each type, which are used as the label data set for model training.

[0105] Subsequently, the terminal can use the cycle to generate the discriminant network. There are 4 models in the method: , , and ,in and Represents a generative model with the same structure. The model composition is as follows Fig.11 As shown, it contains 3 convolution modules, 11 residual modules, a 3x3 convolution layer and an activation layer. and Represents two discriminant models, used to discriminate , The authenticity of the generated images has the same structure, and the model composition is as follows Fig.12 As shown, it contains 4 convolution modules, a pad layer and a 3x3 convolution layer.

[0106] Specifically, the training process is as follows: Fig.10 As shown, X and Y represent the original SEM image and the template image respectively. The network contains 4 models G, F, Dx and Dy. The G network completes the mapping from X to Y, the F network completes the mapping from Y to X, the Dx network determines the authenticity of X generated by the F network, and the Dy network determines the authenticity of Y generated by the G network. The complete optimization objective function is as follows:

[0107] ;

[0108] in:

[0109] ;

[0110] ;

[0111] ;

[0112] Representation Generative Network and the discriminant network The optimization objective function of

[0113] Representation Generative Network and the discriminant network The optimization objective function of

[0114] represents the cyclic loss function between the two generating networks;

[0115] Represents SEM image input network;

[0116] Represents the generated template image input Discriminant network;

[0117] Represents template image input network;

[0118] Represents SEM image input Discriminant network;

[0119] In this way, two generator networks F and G are obtained by training with L (G, F, Dx, Dy) as the optimization target. G is used to generate template images from SEM images, and F is used to generate SEM images from template images.

[0120] In this embodiment, in the training process of the image conversion model, a cyclic loss function between the generative networks is set in the optimization objective function, which helps to support the acceptable reconstruction of the input image through model conversion, thereby enhancing the robustness and reliability of the image conversion model.

[0121] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0122] Based on the same inventive concept, the embodiment of the present application also provides an electron microscopic imaging measurement device for implementing the electron microscopic imaging measurement method involved above. The implementation solution provided by the device to solve the problem is similar to the implementation solution recorded in the above method, so the specific limitations in one or more embodiments of the electron microscopic imaging measurement device provided below can refer to the limitations of the electron microscopic imaging measurement method above, and will not be repeated here.

[0123] In one embodiment, Fig.13 As shown, an electron microscopic imaging measurement device is provided, comprising: a detection image module, a template image module, a target detection module and a measurement parameter module, wherein:

[0124] A detection image module, used to obtain a detection image of a target object to be processed;

[0125] A template image module, used for processing the detection image based on a pre-built image conversion model to obtain a template image matching the detection image;

[0126] A target detection module, used to determine target information of a target area in the template image based on a preset image analysis algorithm, wherein the target area includes a measurement target and a measurement target edge;

[0127] A measurement parameter module is used to generate a measurement target frame corresponding to the target area according to the target information, apply the measurement target frame to the detection image to determine a local area containing the target object in the detection image, and image the local area to achieve measurement of the target object.

[0128] In one embodiment, the target detection module includes:

[0129] A connected domain module, used for traversing the template image and determining a plurality of connected domains in the template image based on pixel value differences in the template image;

[0130] The area identification module is used to assign an area identification to the connected domain, where the area identification is a unique identification.

[0131] In one embodiment, the target detection module includes:

[0132] An initial boundary module, used to identify the boundary of the connected domain based on a preset contour extraction algorithm and obtain initial boundary information;

[0133] The geometric approximation module is used to perform geometric approximation processing on the boundary of the connected domain according to the initial boundary information, and obtain the boundary information of the connected domain that meets the preset geometric constraint conditions.

[0134] In one embodiment, the target detection module includes:

[0135] A pixel trend module, used to obtain the measurement target edge information of the target area and obtain a pixel change trend curve of the measurement target edge;

[0136] The edge type module is used to determine the edge type of the edge of the measurement target according to the pixel change trend curve.

[0137] In one embodiment, before the template image module, it also includes:

[0138] A sample data module, used to obtain a sample data set, wherein the sample data set includes a historical detection image and a corresponding graphic data system template;

[0139] A pattern matching module, used for performing pattern matching between the historical detection image and the graphic data system template, and generating several types of sample template images based on the graphic data system template;

[0140] The conversion model module is used to construct a label data set based on the historical detection images and the sample template images of a preset ratio, and train the pre-constructed loop generation discriminant network model through the label data set to obtain the image conversion model.

[0141] In one embodiment, the conversion model module includes:

[0142] The cyclic generation discriminant network model includes two generation networks and a corresponding discriminant network, wherein the generation network is used to realize the conversion generation between the detection image and the template image, and the discriminant network is used to realize the authenticity discrimination of the generated image;

[0143] The optimization function module is used to construct an optimization objective function, and constrain the training process of the cyclic generation and discrimination network model based on the optimization objective function. The optimization objective function includes the corresponding optimization objective function between the generation network and the discrimination network, and also includes the cyclic loss function between the generation networks.

[0144] Each module in the above-mentioned electron microscopic imaging measurement device can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0145] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Fig.14As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, an electron microscopic imaging measurement method is realized. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0146] Those skilled in the art will understand that Fig.14 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0147] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0148] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0149] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0150] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions.

[0151] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0152] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0153] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. An electron microscopic imaging measurement method, characterized in that: The method comprises: Acquire a detection image of a target object to be processed; Processing the detection image based on a pre-built image conversion model to obtain a template image matching the detection image; Determine target information of a target area in the template image based on a preset image analysis algorithm, wherein the target area includes a measurement target and a measurement target edge; A measurement target frame corresponding to the target area is generated according to the target information, and the measurement target frame is applied to the detection image to determine a local area containing the target object in the detection image, and the local area is imaged to achieve measurement of the target object.

2. The method according to claim 1, characterized in that The target information of the target area in the template image is determined based on a preset image analysis algorithm, wherein the target area includes a measurement target and a measurement target edge, including: Traversing the template image, and determining a plurality of connected domains in the template image based on pixel value differences in the template image; A region identifier is assigned to the connected domain, where the region identifier is a unique identifier.

3. The method according to claim 2, characterized in that The target information of the target area in the template image is determined based on a preset image analysis algorithm, wherein the target area includes a measurement target and a measurement target edge, including: Identify the boundary of the connected domain based on a preset contour extraction algorithm to obtain initial boundary information; A geometric approximation process is performed on the boundary of the connected domain according to the initial boundary information to obtain boundary information of the connected domain that meets preset geometric constraints.

4. The method according to claim 1, characterized in that: The target information of the target area in the template image is determined based on a preset image analysis algorithm, wherein the target area includes a measurement target and a measurement target edge, including: Acquire the measurement target edge information of the target area, and acquire the pixel change trend curve of the measurement target edge; The edge type of the edge of the measurement target is determined according to the pixel change trend curve.

5. The method according to any one of claims 1 to 4, characterized in that: Before the detection image is processed based on the pre-built image conversion model to obtain a template image matching the detection image, the method further includes: Acquire a sample data set, wherein the sample data set includes historical detection images and corresponding graphic data system templates; Performing pattern matching between the historical detection image and the graphic data system template, and generating several types of sample template images based on the graphic data system template; A label data set is constructed based on the historical detection images and the sample template images in a preset ratio, and a pre-constructed loop generation discriminant network model is trained by using the label data set to obtain the image conversion model.

6. The method according to claim 5, characterized in that The step of constructing a label data set based on the historical detection images and the sample template images of a preset ratio, and training a pre-constructed cyclic generation discriminant network model through the label data set to obtain the image conversion model includes: The cyclic generation discriminant network model includes two generation networks and a corresponding discriminant network, wherein the generation network is used to realize the conversion generation between the detection image and the template image, and the discriminant network is used to realize the authenticity discrimination of the generated image; An optimization objective function is constructed, and the training process of the cyclic generation and discrimination network model is constrained based on the optimization objective function. The optimization objective function includes the corresponding optimization objective function between the generation network and the discrimination network, and also includes a cyclic loss function between the generation networks.

7. An electron microscopic imaging measurement device, characterized in that: The device comprises: A detection image module, used to obtain a detection image of a target object to be processed; A template image module, used for processing the detection image based on a pre-built image conversion model to obtain a template image matching the detection image; A target detection module, used to determine target information of a target area in the template image based on a preset image analysis algorithm, wherein the target area includes a measurement target and a measurement target edge; A measurement parameter module is used to generate a measurement target frame corresponding to the target area according to the target information, apply the measurement target frame to the detection image to determine a local area containing the target object in the detection image, and image the local area to achieve measurement of the target object.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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